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dc.creatorsilva d, jesus g
dc.creatorSenior Naveda, Alexa
dc.creatorGarcía Guiliany, Jesús Enrique
dc.creatorNiebles Nuñez, William
dc.creatorHernández Palma, Hugo
dc.date.accessioned2020-01-30T13:48:13Z
dc.date.available2020-01-30T13:48:13Z
dc.date.issued2020
dc.identifier.issn1742-6588
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/11323/5960
dc.description.abstractTraditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.spa
dc.language.isoengspa
dc.publisherJournal of Physics: Conference Seriesspa
dc.relation.ispartof10.1088/1742-6596/1432/1/012031/pdfspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectElectric chargespa
dc.subjectElectrical demandspa
dc.subjectForecasting modelsspa
dc.titleForecasting electric load demand through advanced statistical techniquesspa
dc.typeArticlespa
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dcterms.references[11] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognitionspa
dcterms.references[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.spa
dcterms.references[13] Sanchez L., Vásquez C., Viloria A., Cmeza-estrada (2018) Conglomerates of Latin American Countries and Public Policies for the Sustainable Development of the Electric Power Generation Sector. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.spa
dcterms.references[14] Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., & Henry, M. A. (2018, June). Fault diagnosis on electrical distribution systems based on fuzzy logic. In International Conference on Sensing and Imaging (pp. 174-185). Springer, Cham.spa
dcterms.references[15] Perez, Ramón, Carmen Vásquez, and Amelec Viloria. "An intelligent strategy for faults location in distribution networks with distributed generation." Journal of Intelligent & Fuzzy Systems Preprint (2019): 1-11.spa
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dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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